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3D Point Cloud Multi-target Detection Method Based on PointNet++

Авторы: Li J., Pan B., Cherkashin E., Liu L., Sun Z., Zhang M., Li Q.

Журнал: Advances in Intelligent Systems and Computing

Том: 1274

Номер:

Год: 2021

Отчётный год: 2020

Издательство:

Местоположение издательства:

URL:

Проекты:

DOI: 10.1007/978-981-15-8462-6_147

Аннотация: 3D object detection is an important research direction in the fields of computer vision and pattern recognition in recent years. This technology can provide important technical support for unmanned driving and intelligent robots. Aiming at the challenges of object detection caused by the sparseness of 3D point clouds in outdoor scenes, this paper designs a 3D point cloud multi-target detection method based on pointnet++. The method first preprocesses the collected original point cloud; after obtaining the point cloud of the region of interest, the point cloud is clustered, and then the 3D target detection is performed by pointnet++ to obtain the object category. Finally, get the size and orientation of the target object through the 3D boundingbox. In order to verify the effectiveness of the method in this paper, the point cloud data of real outdoor scenes were collected using lidar, and a sample set was produced for network training. The final results verify that the method can achieve higher detection accuracy and meet the requirements of real-time performance.

Индексируется WOS: Нет

Индексируется Scopus: Нет

Индексируется УБС: Нет

Индексируется РИНЦ: Да

Индексируется ВАК: Нет

Индексируется CORE: Нет

Публикация в печати: 0